Abstract

In this paper, we explore the possibility that machine learning approaches to natural-language processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory as well.

Footnotes

1 We gratefully acknowledge Michael Collins's aid in drafting section 5 of this paper. Antecedents of this paper were presented at the Workshop on Computational Linguistics, University College, London, 2004; Computational Linguistics in the Netherlands 2004, Leiden; the Linguistics Colloquium, University of Illinois at Urbana-Champaign, 2005; Construction of Meaning: Stanford Semantics and Pragmatics Workshop, Stanford University, 2005; the 2005 LSA Summer Institute, Cambridge, MA; ESSLI 2005, Edinburgh; the Computer Science Colloquium, University of Essex, 2006; the Cognitive Science and Linguistics Colloquium, Carleton University, Ottawa, 2006; and the Linguistics Department Colloquium, Stanford University, 2006. The first author presented some of the proposals contained in this paper in his Ph.D Research Seminar in the Philosophy Department, King's College, London, during the second semester, 2006. We are grateful to the participants of these forums and to three anonymous reviewers for the Journal
of
Linguistics for much useful feedback, some of which has led to significant modifications in our proposals. We would also like to thank Joan Bresnan, Alex Clark, Eve Clark, Jennifer Cole, Jonathan Ginzburg, Ray Jackendoff, Dan Jurafsky, Ruth Kempson, Chris Manning, Fernando Pereira, Steve Pinker, Geoff Pullum, Ivan Sag, Richard Samuels, Barbara Scholz, Gabriel Segal, Richard Sproat, and Charles Yang for helpful discussion of many of the ideas proposed in this paper. We are indebted to the Stanford Symbolic Systems Program, Ivan Sag, and Todd Davies for providing a welcoming venue for completion of the paper. The first author is grateful to the Leverhulme Foundation for supporting his work there. The first author's research was supported by grant number RES-000-23-0065 of the Economic and Social Research Council of the United Kingdom. Needless to say, we bear sole responsibility for these ideas.